Does your friends list on Facebook reveal enough about you for a lender to determine that you’re likely to pay back a loan?

For the roughly 60 million people in the U.S. who live their financial lives largely outside the umbrella of mainstream banks, it’s one of many unusual data sets being probed by non-prime lenders to pinpoint credit risk.

And a San Diego company is among those leading the way in developing complex mathematical formulas that tap nontraditional data as a means to credit screen marginal borrowers.

Global Analytics, founded as a consulting outfit in 2003 by Krishna Gopinathan and Purna Patnaik, has operated under the radar in San Diego’s tight-knit analytics community. Like most in the field locally, its top scientists got their start at HNC Software — which developed the powerhouse Falcon Fraud Manager predictive analytics system for unearthing bogus credit-card transactions. HNC was sold to FICO score provider Fair Isaac a decade ago.

Over the years, Global Analytics has built an analytics platform for online lending to borrowers with poor or limited credit histories. It has operated successfully in the United Kingdom since 2008, where the company says it’s the fifth-largest online lender with 250,000 online installment loans processed and about a 10 percent share of a $1 billion market.

The company, which employs 400 full-time and contract workers mostly in India (only about 15 work at the San Diego headquarters) is now talking with partners in the U.S. about licensing or franchising its online credit screening platform called Zebit. It recently raised $25 million in venture capital to fuel its expansion.

“What we’re able to do is supply data and very sophisticated analytics that we call adaptive data fusion to this problem of being able to safely transact with the under-banked,” said Michael Thiemann, Chief Executive of Global Analytics. “And by safely we mean lower fraud, lower credit risk to the financier and for the customer, not having to go to a place with bulletproof glass.”

U.S. market complex

It won’t necessarily be easy for Global Analytics to find a foothold in the U.S. market. There are technology competitors, such as ZestFinance, founded by former Google senior executive Douglas Merrill.

In addition, regulations are more complex in the U.S. than in Britain, with different rules in each state and federal restrictions on what data can be used in underwriting.

“It’s going to be a very competitive space, but they’re smart guys,” said Bruce Hansen, former CEO of ID Analytics, which focused on identity theft fraud but also provided credit screening for payday lenders. “I think the key for them will be do they really have meaningful differentiation, and will they be able to sell technology to their would-be competitors. Granted one is in the U.S. and one is in the U.K., but some companies don’t do business with competitors.”

Thiemann thinks there’s plenty of room, in part because banks have shied away from a growing number of marginal borrowers since the financial crisis. He also thinks there are opportunities to roll out financing tied to particular products. No-contract wireless carriers, for example, could offer financing for expensive smartphones using the Zebit platform.

Moreover, poor or marginal borrowers are increasingly online and adopting smartphones. In March, a Federal Reserve study called “Consumers and Mobile Financial Services” found that 91 percent of the under banked have a mobile phone and 57 percent have a smartphone — rates far above those for the overall population, where only 44 percent own smartphones.

Nontraditional sources

Global Analytics uses traditional credit screening reports and common public information, such as vehicle registration data or phone number and address verification, in its analytics platform. But that seldom yields enough to risk making a loan because of a lack of credit history.

“When you look at the under-banked, there is a shortage of data on this people,” said Gopinathan, Global Analytics’ president and chief operating officer. “They’re often young. They have damaged credit. So when traditional credit bureau data are not available, you have to look for little nuggets and fragments from other sources.”

Those nuggets include things like Web cookie data and social media posts — typically accessed with permission from the prospective borrowers. Web cookies might show if borrowers visit gambling sites or job training URLs. Social media can reveal how many friends a borrower has and how long they’ve had them — signs of stability.

Even the online credit application can shine light. A perfectly filled-out application may indicate responsibility, but if users spend little time reading the terms and conditions, it could set off warning bells for fraud.

‘Adaptive data fusion’

Global Analytics sucks all this data into its machine learning algorithm, which has crunched millions of similar data sets before looking for patterns. It computes different scenarios and spits out terms that give the lender the best chance to get a solid return on capital.

“Adaptive data fusion simply means is using multiple data sources in a real-time mode, and then adapting to changes in that data in real time,” said Thiemann. “But this is a very hard thing to do. There are very few people who have strong expertise in underwriting the under banked.”